Optimising Aperture Shapes for Depth Estimation

Abstract

The finite depth of field of a real camera can be used to estimate the depth structure of a scene. While the distance of an object from the plane in focus determines the defocus blur size, the shape of the aperture determines the shape of the blur. This blur shape can be manipulated by introducing masks into the main lens aperture. We propose an intuitive criterion to design aperture patterns for depth estimation. Our design criterion imposes constraints directly in the data domain and optimises the amount of depth information carried by blurred images. As a quadratic function on the aperture transmission values, our criterion can be numerically evaluated to estimate optimised aperture patterns quickly. The proposed mask optimisation procedure is applicable for different depth estimation scenarios. We consider depth estimation from two images with different focus settings and depth estimation from two images with different aperture shapes.